Natural language processing of unstructured data

ABSTRACT

A computer system for processing unstructured data, the computer system comprising a computer processor, a computer memory operatively coupled to the computer processor and the computer memory having disposed within it computer program instructions that, when executed by the processor, cause the computer system to carry out the steps of receiving unstructured data input from a client device, analyzing the unstructured data for features that satisfy logical segment criteria by using natural language processing (NLP), and partitioning the unstructured data into logical segments based on satisfaction of the logical segment criteria.

BACKGROUND

The present invention generally relates to data processing, and inparticular, natural language processing of unstructured data.

Natural language processing (“NLP”) is a field of computer science,artificial intelligence, and linguistics concerned with the interactionsbetween computers and human (natural) languages. Many challenges in NLPinvolve natural language understanding, e.g., enabling computers toderive meaning from human or natural language input. Understanding humanlanguage includes understanding not only the words, but also theconcepts and how they are organized. For example, unstructured datacomprising a large body of text commonly include various logicalsections.

SUMMARY

A method, computer systems, and computer program products are disclosed.According to one embodiment, said method is for processing unstructureddata in a data processing system comprising a processor and a memory.Said method comprises receiving, by said data processing system,unstructured data input from a client device. Said unstructured data isanalyzed, by said data processing system, for features that satisfylogical segment criteria by using natural language processing (NLP).Said method further comprises partitioning, by said data processingsystem, said unstructured data into logical segments based onsatisfaction of said logical segment criteria.

Said unstructured data may comprise text including a variety of topicsor content. Analyzing said unstructured data for features may furthercomprise using said NLP to identify text that satisfy said logicalsegment criteria. In one embodiment, said unstructured data may includecompliance obligations. Said logical segment criteria may includefeatures associated with a plurality of industries or companies. Saidlogical segment criteria may also include features associated withimportance, priority, or risk.

According to another embodiment, a computer system comprises a computerprocessor, a computer memory operatively coupled to said computerprocessor and said computer memory having disposed within it computerprogram instructions that, when executed by said processor, cause saidcomputer system to carry out said steps of receiving unstructured datainput from a client device, analyzing said unstructured data forfeatures that satisfy logical segment criteria by using natural languageprocessing (NLP), partitioning said unstructured data into logicalsegments based on satisfaction of said logical segment criteria, andincorporating said logical segments into a repository.

Said computer system may further comprise said processor linking one ormore files from said repository to said logical segments. In anotherembodiment, said computer system further comprises said processorgenerating files, documents, records, or data entries using said logicalsegments. Said logical segments may comprise one or more pointers,references, linked lists, or data structures.

According to another embodiment, a computer system comprises a computerprocessor, a computer memory operatively coupled to said computerprocessor and said computer memory having disposed within it computerprogram instructions that, when executed by said processor, cause saidcomputer system to carry out said steps of receiving unstructured datafrom a user input. Said unstructured data is decomposed into textfragments. Logical segment evaluation criteria are received from saiduser input. Features of said text fragments are identified. A score isassigned to said text fragments for one or more logical segments.

Decomposing said unstructured data into text fragments may furthercomprise grouping text fragments based on logical operators, formattingcodes, and punctuation. Said computer system may further comprisecomparing said unstructured data to said logical segment evaluationcriteria. Said logical segment evaluation criteria may define how saidunstructured data is divided into logical segments. Said logicalsegments may represent topics, topic types, target audiences, anddegrees of importance. Identifying features of said text fragments mayfurther comprise using NLP to determine that said text fragments satisfysaid logical segment evaluation criteria. Assigning said score to saidtext fragments may further comprise evaluating said text fragments inaccordance to said logical segment evaluation criteria. Said score maycomprise a value that indicates a degree to which said text matches alogical segment based on said logical segment evaluation criteria.

In one embodiment, a computer program product comprises a computerreadable storage medium having stored thereon program instructionsexecutable by a computer to cause said computer to receive unstructureddata input from a client device. Said computer program product alsocomprises program instructions executable by said computer to cause saidcomputer to analyze said unstructured data for features that satisfylogical segment criteria by using natural language processing (NLP).Said computer program product further comprises program instructionsexecutable by said computer to cause said computer to partition saidunstructured data into logical segments based on satisfaction of saidlogical segment criteria.

Said unstructured data may comprise text including a variety of topicsor content. Said computer program product may further comprise programinstructions executable by said computer to cause said computer to usesaid NLP to identify text that satisfy said logical segment criteria.Said unstructured data may include compliance obligations. Said logicalsegment criteria can include features associated with a plurality ofindustries or companies. Said logical segment criteria may also includefeatures associated with importance, priority, or risk.

In one embodiment, a computer program product comprises a computerreadable storage medium having stored thereon program instructionsexecutable by a computer to cause said device to receive unstructureddata input from a client device. Said computer program product furthercomprises program instructions executable by said computer to cause saidcomputer to analyze said unstructured data for features that satisfylogical segment criteria by using natural language processing (NLP).Said computer program product comprises program instructions executableby said computer to cause said computer to partition said unstructureddata into logical segments based on satisfaction of said logical segmentcriteria. Said computer program product also comprises fourth programinstructions executable by said computer to cause said computer toincorporate said logical segments into a repository.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 2 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 3 depicts a logical block diagram of a system for natural languageprocessing of unstructured data according to one embodiment of thepresent invention.

FIG. 4 depicts an exemplary method for processing unstructured dataaccording to one embodiment of the present invention.

FIG. 5 depicts an exemplary diagram of incorporating logical segmentsinto a repository according to one embodiment of the present invention.

FIG. 6 depicts an exemplary method for natural language processingaccording to an embodiment of the present invention.

DETAILED DESCRIPTION

Subject matter will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, exemplary embodiments in which theinvention may be practiced. Subject matter may, however, be embodied ina variety of different forms and, therefore, covered or claimed subjectmatter is intended to be construed as not being limited to any exampleembodiments set forth herein; example embodiments are provided merely tobe illustrative. It is to be understood that other embodiments may beutilized and structural changes may be made without departing from thescope of the present invention. Likewise, a reasonably broad scope forclaimed or covered subject matter is intended. Throughout thespecification and claims, terms may have nuanced meanings suggested orimplied in context beyond an explicitly stated meaning. Likewise, thephrase “in one embodiment” as used herein does not necessarily refer tothe same embodiment and the phrase “in another embodiment” as usedherein does not necessarily refer to a different embodiment. It isintended, for example, that claimed subject matter include combinationsof exemplary embodiments in whole or in part. Among other things, forexample, subject matter may be embodied as methods, devices, components,or systems. Accordingly, embodiments may, for example, take the form ofhardware, software, firmware or any combination thereof (other thansoftware per se). The following detailed description is, therefore, notintended to be taken in a limiting sense.

Exemplary methods, computer systems, and products for natural languageprocessing (“NLP”) in accordance with the present invention aredescribed with reference to the accompanying drawings. NLP can be usedto analyze text in combination with machine-learning to facilitateunderstanding of human languages by a computer. Computers may utilizeNLP in a variety of real-world applications, such as, machinetranslation, information extraction, automatic text summarization,sentiment analysis, word filtering, automated question answering, etc.According to embodiments of the present invention, NLP may be used toextract and separate information pertaining to a variety of topics,genres, or subject matter from unstructured data into logical segments.Information in the logical segments may then be added into or mapped tocorresponding documents of a corpus or database. Unstructured data mayinclude information that either does not have a pre-defined data modelor is not organized in a pre-defined manner and is typically text-heavy,but may contain data such as dates, numbers, and facts. Irregularitiesand ambiguities in unstructured data make it difficult to process ascompared to organized data such as fielded data stored in databases ordata that has been annotated (semantically tagged) such as in documents.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and unstructured data processing 96.

FIG. 3 presents a logical block diagram of a system for natural languageprocessing of unstructured data according to one embodiment of thepresent invention. The present invention is not limited to thearrangement of servers and other devices in the exemplary systemillustrated in FIG. 3, but rather are for explanation. Data processingsystems useful according to various embodiments of the present inventionmay include additional servers, routers, other devices, and peer-to-peerarchitectures, not shown in FIG. 3, as understood by those of skill inthe art.

The system includes a client device 102 and corpus data server 104communicatively coupled to server 106 via a network 108. Client device102 may comprise computing devices (e.g., desktop computers, terminals,laptops, personal digital assistants (PDA), cellular phones,smartphones, tablet computers, or any computing device having a centralprocessing unit and memory unit capable of connecting to a network).Client devices may also comprise a graphical user interface (GUI) or abrowser application provided on a display (e.g., monitor screen, LCD orLED display, projector, etc.). A client device may include or execute avariety of operating systems, such as personal computer operatingsystems (e.g., Windows, Mac OS or Linux, etc.), mobile operating systems(e.g., iOS, Android, or Windows Mobile, etc.), or the like. A clientdevice may include or may execute a variety of possible applications,such as a client software application enabling communication with otherdevices, such as communicating one or more messages, such as via email,short message service (SMS), or multimedia message service (MIMS).

The system further includes automated computing machinery comprising theserver 106 useful in natural language processing according toembodiments of the present invention. The server includes at least onecomputer processor or “CPU” as well as random access memory (“RAM”)which is connected through a high-speed memory bus and bus adapter toprocessor and to other components of the server. Stored in RAM, or ahard drive connected to the RAM, may be a content analyzer 114 includingcomputer program instructions that, when executed, cause the computer toperform natural language processing according to embodiments of thepresent invention by extracting specific topics of information fromunstructured text into logical segments.

The content analyzer 114 may comprise an artificial intelligence unittrained by model trainer 112 (e.g., using machine learning techniquessuch as neural networks) to identify text of unstructured data belongingto certain logical segment classifications. Training data may bereceived from various entities in various ways, including, for example,from a user through a graphical user interface (“GUI”) presented on thedisplay of the client device 102 and/or from corpus data server 104 forthe purpose of gathering and compiling training data. Unstructured datamay be provided from user input 110. The unstructured data may include adata structure that includes a description of terms or a combination ofterms, acronyms, numbers, codes, or phrases, and so on. The elements ofthe unstructured data may be compared to criteria to determine if theelements meet a logical segment classification. Logical segments mayrepresent, for example, topics, topic types, target audiences, degreesof importance, etc.

Corpus data server 104 may comprise a computing device operable toprovide a source of both structured and unstructured data from, forexample, files, documents, tables, charts, illustrations, photographs,etc. According to one embodiment, client device 102 may provideunstructured data to user input 110 based on data retrieved from corpusdata server 104. The content analyzer 114 can be configured to receivethe unstructured data from user input 110 to process. The unstructureddata may comprise text including elements against which criteria oflogical segment classifications may be measured or otherwise compared.The text may fulfill criteria to meet in order to qualify as text thatrelates to various logical segments. The unstructured data from the userinput 110 may be partitioned or divided into logical segments by thecontent analyzer 114 and sorted into records corresponding to thelogical segments in database 118. Linker 116 is operable to add or mapthe logically segmented data to corresponding documents or file indatabase 118.

Stored in RAM also is an operating system. Operating systems useful fornatural language processing according to embodiments of the presentinvention include UNIX™ Linux™ Microsoft Windows™ AIX™ IBM's i5/OS™ andothers as will occur to those of skill in the art. Non-volatile computermemory also may be implemented for such as an optical disk drive,electrically erasable programmable read-only memory (so-called ‘EEPROM’or ‘Flash’ memory), RAM drives, and so on, as will occur to those ofskill in the art.

Network 108 may be any suitable type of network allowing transport ofdata communications across thereof. Network 108 may support many datacommunications protocols, including for example TCP (TransmissionControl Protocol), IP (Internet Protocol), HTTP (HyperText TransferProtocol), WAP (Wireless Access Protocol), HDTP (Handheld DeviceTransport Protocol), and others as will occur to those of skill in theart. The network 108 may couple devices so that communications may beexchanged, such as between servers and client devices or other types ofdevices, including between wireless devices coupled via a wirelessnetwork, for example. A network may also include mass storage, such asnetwork attached storage (NAS), a storage area network (SAN), cloudcomputing and storage, or other forms of computer or machine readablemedia, for example. In one embodiment, the network may be the Internet,following known Internet protocols for data communication, or any othercommunication network, e.g., any local area network (LAN) or wide areanetwork (WAN) connection, cellular network, wire-line type connections,wireless type connections, or any combination thereof. Communicationsand content stored and/or transmitted to and from client devices andservers may be encrypted using, for example, the Advanced EncryptionStandard (AES) with a 128, 192, or 256-bit key size, or any otherencryption standard known in the art.

FIG. 4 depicts an exemplary method for processing unstructured dataaccording to one embodiment of the present invention. Unstructured datainput is received from a user of a client device, step 202. Theunstructured data may comprise text or characters including a variety oftopics or content. The unstructured data is analyzed, step 204. Inparticular, the unstructured data may be parsed for specific featureswithin a body of information. The specific features may be selected froma library of criteria for the specific features (to be placed intological segments). The specified criteria may define how theunstructured data may be divided into logical segments. NLP may be usedto analyze the unstructured data input to identify text that may satisfythe criteria.

For example, unstructured data may include large bodies of text such ascompliance obligations including laws, regulations, contractualcommitments, organizational and industry standards, codes or practice,ethical codes of conduct, good governance guidelines, and agreementswith community groups or non-governmental organizations. Thesecompliance obligations may typically comprise several sections andguidance. When an obligation applies to an organization, a subset of thetext may apply to a first business line (e.g., risk assessment) of theorganization while a second subset may apply to another business line(e.g., compliance). A user may specify obligations as features that arerelevant to certain industries or companies. The user may also specifyfeatures that are more important than others, or present a certaindegree of priority or risk.

The unstructured data is partitioned into logical segments, step 206.Text from the unstructured data can be partitioned or identified asbelonging to one or more logical segments based on satisfaction of oneor more specified criteria. Referring to the previous example, acompliance obligation may be analyzed and divided into a set of sectionsbased on relevancy to a line of business (e.g., either risk assessmentor compliance). For each logical segment, the method may identify atleast one business line for which the requirements set forth in thesection must be complied.

Logical segments are incorporated into a repository, step 208. Thelogical segments of text from the unstructured data may be applied toexisting data and files in the repository. For example, the logicalsegments of text from the unstructured data may be linked to the dataand files in the repository. Alternatively, the logical segments of textfrom the unstructured data may be used to create new files, documents,records, or data entries in the repository. The data and files in therepository may be associated with functions, such as, for granularpolicy execution and guidance linking. Again, referring to the previousexample, the logical segments may be assigned to applicable businesslines. In another embodiment, a set of obligations can be linked to thelogical segments, and the business line may be consequently assigned tothe obligations that are linked to the logical segments.

FIG. 5 presents an exemplary diagram of incorporating logical segmentsinto a repository according to one embodiment of the present invention.Unstructured data 302 may be logically partitioned or identified intological segments 304A, 304B, 304C using, for example, NLP and machinelearning. Alternatively, logical segments 304A, 304B, and 304C may becreated to include particular sections of data or text from unstructureddata 302. Logical segments 304A-304C may comprise pointers, references,linked lists, data structures, or any combination thereof.

Repository 306 includes data entry 308A, 308B, and 308C. The logicalsegments 304A-304C may be associated with the data entries in repository306. As illustrated, logical segments 304A-304C are respectively mappedor referenced to data entries 308A-308C. Accordingly, when a given dataentry is retrieved or accessed, it may include a reference to thelogical segments. Such associations may be either temporary orpermanent.

FIG. 6 presents an exemplary method for natural language processingaccording to an embodiment of the present invention. Unstructured datainput is received, step 402. The unstructured data may include textentered via user input. The unstructured data is decomposed into textfragments, step 404. The text may be decomposed into text fragments bygrouping text segments based on logical operators, formatting codes(e.g., paragraph markers, font styles, indentions, tabs and the like),as well as the use of punctuation (e.g., periods, commas, hyphens,semicolons, colons, and the like). Such formatting and punctuation oftenindicates structure to the text fragments.

Logical segment evaluation criteria are received, step 406. Theunstructured data may be compared to criteria to determine if theelements meet a logical segment classification. Logical segments mayrepresent, for example, topics, topic types, target audiences, degreesof importance, etc. The logical segment evaluation criteria may definehow the unstructured data may be divided into the logical segments.Features of the text fragments are identified, step 408. Each textfragment may be analyzed to identify features within the text fragment.NLP may be used to determine text fragment that may satisfy thecriteria.

Scores are assigned to the text fragments for each logical segment, step410. The text fragments may be assigned scores based on an evaluation ofthe text fragments in accordance to the logical segment evaluationcriteria. The scores may comprise a value that indicates a degree towhich a classification of the text matches a logical segment. In such anembodiment, the score may be calculated by first evaluating the veracityof text fragments coupled by logical operators and averaging thesevalues along with the values of text fragment evaluations.

The logical segment evaluation criteria, in some embodiments, mayspecify values to assign in light of certain features of the textfragments. For example, the logical segment classifications may suggestthat a section is related to taxes and the criteria may require that theparagraphs relates to taxes. In this case, logical segment criteria mayspecify a value of 1 (or 100%) that can be assigned to the evaluation ofthat criteria. In other embodiments, scores for text fragments may bedetermined based on whether the text fragments are true in light oflogical operators. For example, a text fragment that may satisfy twocriteria “taxes” and “compliance” and in which the logical operator isan “and” operator, it may be determined that each criteria is true,assigning a value of ‘1’ to each criteria.

The unstructured data is partitioned into logical segments based on thescores, step 412. Certain sections of the unstructured data, such as,the text fragments may be placed into the logical segments in accordanceto the scores associated with the logical segment evaluation criteria.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIGS. 1 through 6 are conceptual illustrations allowing for anexplanation of the present invention. Notably, the figures and examplesabove are not meant to limit the scope of the present invention to asingle embodiment, as other embodiments are possible by way ofinterchange of some or all of the described or illustrated elements.Moreover, where certain elements of the present invention can bepartially or fully implemented using known components, only thoseportions of such known components that are necessary for anunderstanding of the present invention are described, and detaileddescriptions of other portions of such known components are omitted soas not to obscure the invention. In the present specification, anembodiment showing a singular component should not necessarily belimited to other embodiments including a plurality of the samecomponent, and vice-versa, unless explicitly stated otherwise herein.Moreover, applicants do not intend for any term in the specification orclaims to be ascribed an uncommon or special meaning unless explicitlyset forth as such. Further, the present invention encompasses presentand future known equivalents to the known components referred to hereinby way of illustration.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer system for natural language processing(NLP), the computer system comprising a computer processor, a computermemory operatively coupled to the computer processor and the computermemory having disposed within it computer program instructions that,when executed by the computer processor, cause the computer system tocarry out the steps of: receiving unstructured data from a user input;decomposing the unstructured data into text fragments; receiving logicalsegment evaluation criteria from the user input; identifying features ofthe text fragments; and assigning a score to the text fragments for oneor more logical segments.
 2. The computer system of claim 1 whereindecomposing the unstructured data into text fragments further comprisesthe computer processor grouping text fragments based on logicaloperators, formatting codes, and punctuation.
 3. The computer system ofclaim 1 further comprising the computer processor comparing theunstructured data to the logical segment evaluation criteria.
 4. Thecomputer system of claim 1 wherein the logical segment evaluationcriteria define how the unstructured data is divided into logicalsegments.
 5. The computer system of claim 4 wherein the one or morelogical segments represent topics, topic types, target audiences, anddegrees of importance.
 6. The computer system of claim 1 whereinidentifying features of the text fragments further comprises thecomputer processor using NLP to determine that the text fragmentssatisfy the logical segment evaluation criteria.
 7. The computer systemof claim 1 wherein assigning the score to the text fragments furthercomprises the computer processor evaluating the text fragments inaccordance to the logical segment evaluation criteria.
 8. The computersystem of claim 1 wherein the score comprises a value that indicates adegree to which the text fragments matches a logical segment based onthe logical segment evaluation criteria.